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SPATIALLY VARYING AUTOREGRESSIVE PROCESSES
Autoregressive models
Spatial models
Time series
Spacetime
Covariance
Multilevel models
Inference
Predictive modeling
Ecological modeling
Afiliación
Oswaldo Cruz Foundation. Presidency. Scientific Computing Program. Rio de Janeiro, RJ, Brazil.
University of California at Santa Cruz. Department of Applied Mathematics and Statistics. Santa Cruz, CA, USA.
Universidade Federal do Rio de Janeiro. Instituto de Matemática. Rio de Janeiro, RJ, Brasil.
University of California at Santa Cruz. Department of Applied Mathematics and Statistics. Santa Cruz, CA, USA.
Universidade Federal do Rio de Janeiro. Instituto de Matemática. Rio de Janeiro, RJ, Brasil.
Resumen en ingles
We develop a class of models for processes indexed in time and space that are based on autoregressive (AR) processes at each location. We use a Bayesian hierarchical structure to impose spatial coherence for the coefficients of the AR processes. The priors on such coefficients consist of spatial processes that guarantee time stationarity at each point in the spatial domain. The AR structures are coupled with a dynamic model for the mean of the process, which is expressed as a linear combination of time-varying parameters. We use satellite data on sea surface temperature for the North Pacific to illustrate how the model can be used to separate trends, cycles, and short-term variability for high-frequency environmental data. This article has supplementary material online.
Palabras clave en ingles
Time series modelsAutoregressive models
Spatial models
Time series
Spacetime
Covariance
Multilevel models
Inference
Predictive modeling
Ecological modeling
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